Sunday, December 23, 2012

From Payola to Blogola to Pandola?

Montgomery and Moe showed that it would be efficient for labels to pay for airplay (Montgomery & Moe, 2002). This is not surprising given the impact airplay can have on sales, at least in the old music industry (Dertouzos, 2008). However, direct undisclosed payments to influence radio programming (called Payola, a combination of pay and Victrola) are prohibited by law. Hence, labels developed strategies to circumvent the law by using independent promoters or naming payments “consultant fees”.

The bottom line is, there will always be efforts in whatever form to get exposure through a promotion channel if it could increase profits or benefit an artist's career. This principle prevails, as for instance in form of’s Powerplay or Jango’s Airplay program, where artists can simply pay for plays. Or Blogola, where influential bloggers receive anything from free products, to tickets and money in return for featuring a specific artist on their blog.

Today, DJs are replaced by recommendation systems. So the question is: who to pay? I doubt the recommendation algorithm is up for free tickets, money, coke and girls …

What I don’t doubt is that some kind of a recommendation engine optimization strategy will evolve advising music creators how to produce and structure a song in order to maximize plays for a specific audience. Let me name it “Pandola” (Pandora + Payola = Pandola).

Sure it is not for every artist. There have been always two types of artists: The ones serving the crowds, and, on the other hand, the ones serving their own artistic egos. Obviously, Pandola would only be considered by the fist kind.

Would it be a bad thing?

Intuitively yes as it supposedly promotes a musical monoculture, where every song sounds the same. However, clever recommendation systems learn from consumers’ listening behaviors and incorporate feedback. For example, if the system presents 3 songs that are rather similar, I vote “thumps down” on the third song. Hopefully the algorithm learns and recommends songs that are slightly different.

Assuming the algorithm accurately knows my listening preferences (maybe I can switch between different modes, like curiosity and non-curiosity mode), then Pandola could be a good thing. For years, the music industry has been accused for its top-down approach, imposing tastes on consumers. Pandola, on the contrary, depicts a bottom-up approach, where consumers shape the music. If recommendation algorithms learn from consumers’ listening behaviors and music producers learn from recommendation algorithms, we may end-up in music wonderland...

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Monday, December 3, 2012

Some thoughts about where we are regarding digital distribution in 2012 (excerpt of an upcoming book chapter)

Digital distribution has surpassed physical distribution in key markets like the USA or UK and, thus, established as the dominant distribution practice.

While the media often speaks of the next killer app that will revolutionize music consumption and dominate music distribution, the analysis of the current situation suggests that different forms of distribution will coexist. The reason is that consumers value music differently, prefer different formats and expect different experiences from consumption. The same applies to content providers. Each artist or record label is in a specific situation pursuing different goals. As a result, a variety of different distribution models have emerged, each with pros and cons for a certain situation. For content providers it is important to choose the appropriate distribution model that supports the overall strategy.

The most striking development in digital distribution is the blurring line between promotion and distribution. This refers to the fact that, on the one hand, lowering barriers to access music creates a strong promotional effect due to the facilitation of rapid music circulation. On the other hand, heightening the barriers to access music causes an artificial scarcity through excludability, which is essential to implement a business model based on selling musical recordings. The following graphic illustrates this trade-off and the position of each distribution model.

Trade-off between promotion effect and direct revenues
Figure 3: Trade-off between promotion effect and direct revenues

Different distribution models can also be combined parallel or subsequently. In this way, content providers can apply different strategies in different career stages or for specific consumer groups with particular listening preferences. However, if applied at the same time, cannibalization effects may eventually occur. For example, if an artist distributes her music via the free model and a pay-per-download model at the same time, the free model will likely cannibalize revenues of the pay-per-download channel.

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